TITLE:
Neural Nets for Stock Indices: Investigating Effect of Change in Hyperparameters
AUTHORS:
Sonali Agarwal, Jwaad Akhtar Khan
KEYWORDS:
Deep Neural Networks, Multi-Layer Perceptron, Feed Forward Neural Network, Prediction Models, Hyperparameters, Backpropagation, Activation Function, Hidden Layer, RBF Neural Network, Epoch, Iteration, Batch Size, Stock Market, BSE Sensex
JOURNAL NAME:
Theoretical Economics Letters,
Vol.9 No.3,
March
15,
2019
ABSTRACT: Artificial neural networks have seen an outburst of
interest in past decade. There has been an increasing use of ANNs in prediction
based studies owing to their huge performance accuracy. They have been
successfully applied across various domains like medicine, geology, finance,
physics, engineering etc. The system of neural nets witnesses rise in
complexity with increase in number of layers and number of neurons and
possesses the capacity to solve intricate problems. The researchers, world
over, consider the neural network with three layers (input, hidden and output)
a universal approximator of functions as it has given outstanding results in
data validation, price forecasting, sales forecasting, customer research etc.
over the years. In most of the previous studies, either a standard ANN model
has been taken or a default model has been tested using various softwares. But
as we understand, a lot of hit and trial should be done by altering the
hyperparameters to get the best performance model. In our study we attempt to
prove the same point and try to find the best model for our data set wherein we
predict the BSE sensex closing price of the next day using previous day data
(high price, low price, open price, close
price and trade volume). We use deep neural networks with
backpropagation and have altered the hyperparameters: number of nodes in hidden
layers, the activation function of hidden layers, Number of epochs, the batch
size and hence the iterations in each epoch. The model performance was measured
with the help of root mean square error on test set of the model. We are able
to bring out the differences of tuning of hyperparameter and ultimately find
the best predictor model for BSE sensex close value.